This sentiment analysis has been done through the use of the “Bing” lexicon.
This lexicon was first published in:
Minqing Hu and Bing Liu, “Mining and summarizing customer reviews.”, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004), Seattle, Washington, USA, Aug 22-25, 2004.
For more info on Bing: https://rdrr.io/cran/tidytext/man/sentiments.html
The sentiment wordcloud was built using the “BING” lexicon to categorise the overall sentiment into positive and negative words.
For more info on Bing: https://rdrr.io/cran/tidytext/man/sentiments.html
This sentiment analysis has been done through the use of the “Bing” lexicon and combines the overall sentiment on a timeline based on the total daily words in tweets for both the Media Houses and the SA Government
This sentiment graph displays the proportion of daily sentiment in an an area plot in order to gain a clearer visual representation of the difference in sentiment. This sentiment analysis has been done through the use of the “Bing” lexicon and also displays the sentiment over a timeline.
This sentiment analysis has been done through the use of the “Bing” lexicon. The overall sentiment is calculated by subtracting the amount of negative words from the amount of positive words in order to obtain the difference and identify which sentiment category appears the most.
The Government and Media House bar charts are based on the “NRC” lexicon that displays the amount of words placed under the various sentiment categories in descending order.
For more on NRC: https://rdrr.io/cran/lexicon/man/nrc_emotions.html
This plot is based on the “AFINN” lexicon and displays the overall sentiment for all the various twitter accounts.The AFINN lexicon assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.
For more info on AFINN visit: http://www2.imm.dtu.dk/pubdb/pubs/6010-full.html